In order to obtain a social support ecosystem, mobile spatial statistics is an emerging research field focused on tracking a user's mobility using data from cellular phones.
Today, cellular phones are carried and used by almost everyone. Even while they are not actively used, cellular phones transmit certain periodic event data to their associated base stations (BSs) as its registration, location area update, and keep alive messages. These messages are captured at the base station and provide sector-level location information for the users at a given time. The mobile network operators, upon collecting such event data from all their subscribers, may analyze these data and extract useful information. Such information may be helpful for improving urban planning, traffic planning, and disaster prevention. Another example use of the mobile-phone event data, along with some other accompanying information (e.g., gender, age etc. of the subscribers), is to obtain important information such as age/gender/demographic characteristics/address distributions within a given geographical area and time interval, which are normally gathered through the time-consuming census process periodically performed by governments.
Several objectives can be achieved using the operational data of the subscribers to realize the above-mentioned applications: 1) obtaining the geographical distribution of subscribers at a given time instant (hourly, daily, weekly, monthly, etc.), and 2) obtaining the flow of people between different geographical areas. For the first objective, the goal is to obtain the population in a municipality (or mesh, hexagonal sector, etc.) at a given time of the day, while the goal for the second objective is to determine the number of people flowing into a municipal/mesh/sector, their stay times, and their movement distance.
Accurately achieving these objectives using the mobile-phone operational data is a challenging task due to the limited information available in the event data. The event data transmitted by the mobile phones only provide a sector-level location information, where the sector size may range from few hundreds of meters to few kilometers. This is different than a GPS signal, and does not provide the most accurate of location information even if the mobile-phone sends hundreds of event data. Accurate mapping of a subscriber's location within a given sector requires non-trivial signal processing techniques that, for example, involve the use of geographical information systems (GIS) data, some user's trajectory source/destination position, and estimated trajectory. A second important challenge is that the event data is collected with low frequency.
The periodic messages (e.g., periodic location update) are transmitted by the user equipments (UEs) on time intervals that will be on the order of an hour, and the exact frequency of periodic messages can be customized. While a longer time interval between two periodic messages provides lower messaging overhead and less battery consumption at the UE, it also limits the tracking accuracy of the users.
If a UE is mobile and crosses the boundary of a location area (LA) which is composed of several sectors, the UE transmits another operational message referred to as a location update (LAU) message to its associated BS which will be located at the next location area.
A third example for the event data transmitted by the UE are power-on and power-off messages for the UEs. Compared to the periodic message and LAU messages, these are less frequently transmitted, but provide sector-level location information for a UE in a way similar to the periodic message and the LAU message. The other examples for the operational messages transmitted by the UE are phone call/receive and SMS message sent/receive.
Since the use of mobile spatial statistics to obtain population counting/tracking is a relatively new research area, there are only limited number of related works available in the literature. Many of the available prior art references that are related to mobile spatial statistics are about traffic monitoring systems. Such prior art references identify the traffic jams and congestion in an on-line manner using the operational data of the UE in a cellular system. These operational data is then shared among the users who would like to optimize their travel time with the knowledge of the traffic jam information. In order to estimate the traffic jams, the prior art accurately estimates the velocities of the mobile users, sometimes with the help of GIS data. However, the goal in these prior art references is not to track individual users' trajectories, but to detect traffic congestions.
Other prior art references disclose generating trajectories from mobile phone data have been discussed. In particular, one prior art reference discloses a general framework for estimating the trajectories from mobile phone's operational data. As disclosed, given the GIS data and the location area code (LAC) sequences of the users, the Needleman-Wunsch algorithm is applied to determine the best GIS sequence corresponding to the trajectory samples. The basic goal is to compare a given estimated LAC trajectory sequence with various possible GIS sequences, and find the best sequence match. Moreover, a concept of geographical mesh is not used, and the algorithm tries to find trajectories between different LACs. Another prior art reference discloses generating origin-destination matrices from mobile phone's trajectories.
Other prior art references disclose methods of estimating the shortest-path trajectory between an origin and a destination. Possible shortest path algorithms considered in these prior arts are the Dijkstra's algorithm, the A* algorithm, and the Dempster-Shafer method. However, typical applications of these methods are online shortest-path route estimation and recommendation to the user for choosing the best path, e.g., for car navigation. No notion of a geographical mesh is disclosed. Moreover, the available location data samples in these references are typically obtained from GPS devices rather than mobile-phone's operational data. The GPS information provides accurate location information. On the other hand, not all the UEs are equipped with GPS devices. Even if GPS is embedded in the UE, not all users allow the GPS information to be used by the operator. Therefore, the usage of GPS information requires additional complexities such as protecting user's privacy to transfer the location data from the UEs to the BSs (e.g., network) as opposed to the already existing operational data of the UE. This is because the operational data generated by the UE is inevitable information required to establish communications between the UE and the network. How to apply the shortest path algorithms with the limitations of the UE's operational data in consideration is not a trivial task.